Target-Centered Drug Repurposing Predictions of Human Angiotensin
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viruses Article Target-Centered Drug Repurposing Predictions of Human Angiotensin-Converting Enzyme 2 (ACE2) and Transmembrane Protease Serine Subtype 2 (TMPRSS2) Interacting Approved Drugs for Coronavirus Disease 2019 (COVID-19) Treatment through a Drug-Target Interaction Deep Learning Model Yoonjung Choi 1, Bonggun Shin 1 , Keunsoo Kang 2 , Sungsoo Park 1 and Bo Ram Beck 1,* 1 Deargen, Inc., Daejeon 34051, Korea; [email protected] (Y.C.); [email protected] (B.S.); [email protected] (S.P.) 2 Department of Microbiology, College of Natural Sciences, Dankook University, Cheonan 31116, Korea; [email protected] * Correspondence: [email protected] Received: 5 September 2020; Accepted: 16 November 2020; Published: 18 November 2020 Abstract: Previously, our group predicted commercially available Food and Drug Administration (FDA) approved drugs that can inhibit each step of the replication of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) using a deep learning-based drug-target interaction model called Molecule Transformer-Drug Target Interaction (MT-DTI). Unfortunately, additional clinically significant treatment options since the approval of remdesivir are scarce. To overcome the current coronavirus disease 2019 (COVID-19) more efficiently, a treatment strategy that controls not only SARS-CoV-2 replication but also the host entry step should be considered. In this study, we used MT-DTI to predict FDA approved drugs that may have strong affinities for the angiotensin-converting enzyme 2 (ACE2) receptor and the transmembrane protease serine 2 (TMPRSS2) which are essential for viral entry to the host cell. Of the 460 drugs with Kd of less than 100 nM for the ACE2 receptor, 17 drugs overlapped with drugs that inhibit the interaction of ACE2 and SARS-CoV-2 spike reported in the NCATS OpenData portal. Among them, enalaprilat, an ACE inhibitor, showed a Kd value of 1.5 nM against the ACE2. Furthermore, three of the top 30 drugs with strong affinity prediction for the TMPRSS2 are anti-hepatitis C virus (HCV) drugs, including ombitasvir, daclatasvir, and paritaprevir. Notably, of the top 30 drugs, AT1R blocker eprosartan and neuropsychiatric drug lisuride showed similar gene expression profiles to potential TMPRSS2 inhibitors. Collectively, we suggest that drugs predicted to have strong inhibitory potencies to ACE2 and TMPRSS2 through the DTI model should be considered as potential drug repurposing candidates for COVID-19. Keywords: COVID-19; SARS-CoV-2; coronavirus; deep learning; drug repurposing; ACE2; TMPRSS2 1. Introduction Coronavirus disease 2019 (COVID-19) pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) [1] has become a serious threat to public health management. At least 55 million cases have been reported in more than 210 countries and territories for about eight months since December 2019, and the COVID-19 outbreak is currently in progress (Worldometer.info, Viruses 2020, 12, 1325; doi:10.3390/v12111325 www.mdpi.com/journal/viruses Viruses 2020, 12, 1325 2 of 11 17 November 2020). While it is urgent to develop vaccines and treatments to prevent the spread of viral infections, unfortunately, no effective drug or therapy has been found for COVID-19 yet despite extensive efforts by researchers around the world except remdesivir (GS-5734). Remdesivir, developed as a treatment for Ebola virus disease, has been approved for emergency use in patients with severe COVID-19 by U.S. Food and Drug Administration (FDA) [2]. More recently, only remdesivir, now commercially named as Veklury, has been approved as the first treatment for COVID-19 by FDA (FDA press release, www.fda.gov, 22 October 2020). Multinational randomized clinical trials show that remdesivir does not make a significant difference in mortality, but is known to shorten the recovery period for patients [3]. However, more effective medications are still necessary for patients suffering from symptoms of SARS-CoV-2 infection. Our group previously predicted several antiviral (atazanavir, remdesivir, lopinavir/ritonvir) and non-antiviral drugs (rapamycin, tiotropium) that might inhibit SARS-CoV-2 using a new deep drug–target interaction (DTI) prediction model called Molecule Transformer (MT)-DTI [4]. In the previous analysis, these drugs showed strong binding affinity values for viral proteins of SARS-CoV-2. Indeed, several antiviral drugs, such as atazanavir and remdesivir, predicted by MT-DTI, showed potent in vitro and in vivo inhibitory effects on SARS-CoV-2 infection [5,6]. Recently, we further examined that top-ranked non-antiviral drug, tiotropium bromide, has an inhibitory effect on some of the SARS-CoV-2-induced genes through transcriptome-based analysis [7]. While analyzing tiotropium bromide, it was suggested that there is a possibility of more therapeutic targets and options for COVID-19 that may be predicted by deep learning technology to fight against the on-going pandemic. To carry out the task, angiotensin-converting enzyme 2 (ACE2) and transmembrane protease serine subtype 2 (TMPRSS2) that the most studied host targets at the moment, were selected. SARS-CoV-2 uses the ACE2 receptor and TMPRSS2 for infection into host cells [8,9]. ACE2, which serves as a binding receptor of the spike protein of SARS-CoV-2, involves in blood pressure regulation and cardiovascular regulation through enzymatic hydrolysis of angiotensin (Ang) II to Ang (1–7). Ang II cleavage by ACE2 counteracts the function of ACE that promotes Ang II formation [10,11]. Interestingly, since ACE2 is already known as a binding receptor for SARS-CoV [12], it supports that SARS-CoV-2, which maintains a very close evolutionary relationship with SARS-CoV, also utilizes ACE2 for host cell entry. Another proposed target protein, TMPRSS2, is a cell membrane-bound serine protease [13] which function is yet unclear since its identification, but the involvement of TMPRSS2 during SARS-CoV-2 entry is suggested as the host protease for the viral spike-ACE2 complex proteolytic activation [8,14]. Due to the role of ACE2 and TMPRSS2, which are essential for virus entry, they have been proposed as host targets to block SARS-CoV-2 entry [14]. Therefore, in this study, we predicted drug candidates that can control these two potential targets by using a deep-learning approach. 2. Materials and Methods 2.1. Drug Screening Database Design Approved small molecule drugs were collected from DrugBank database [15] and listed for the drug screening database of the present study. Then, all stereoisomers of approved drugs were collected from ZINC database [16] and merged to the drug screening database by using simplified molecular-input line-entry system (SMILES) strings as queries. SMILES strings identical in both DrugBank and ZINC database were treated as approved isomers, and drugs with no SMILES match were treated as racemates (e.g., thalidomide, lenalidomide, pomalidomide, etc.). A total of 1400 drugs were selected for the screening process. 2.2. Affinity Prediction of Each Drug to ACE2 and TMPRSS2 A drug-target interaction deep learning model previously described as MT-DTI was used for the affinity prediction [17]. The model is pretrained with approximately 100,000,000 SMILES of chemicals Viruses 2020, 12, 1325 3 of 11 in PubChem database [18] to produce a better chemical representation that considers global contexts of a given SMILES sequence through a self-attention mechanism. This self-attention mechanism proved its effectiveness in many natural language process tasks compared to local context-based models. With the pretrained MT chemical representation and a convolutional neural network (CNN) protein amino sequence representation, the MT-DTI model was trained with a combined and curated chemical-protein pairs from the Drug Target Common database [19] and BindingDB database [20] to predict affinity in Kd value of given chemical-protein pairs. The performance evaluation (Concordance index, 0.882 and 0.887 in KIBA and DAVIS datasets; mean squared error, 0.152 and 0.245 in KIBA and DAVIS dataset) and comparison to other models of MT-DTI is reported in Shin et al. (2019). Finally, the screening database was processed to the model with amino acid sequences of human ACE2 (UniProt entry Q9BYF1, NCBI Reference Sequence: NP_001358344.1) and TMPRSS2 (UniProt entry O15393, NCBI Reference Sequence: NP_001128571.1) as targets. After the prediction, results were screened for drugs with affinity Kd < 100 nM to each target protein. Drugs approved as racemic mixture were excluded as they cannot be expressed in one SMILES string and may have differential ratio of isomers and biological outcomes per each isomer in the human. DTI prediction results without any filters are provided as supplementary materials. 2.3. Cross-Prediction of MT-DTI Results through AutoDock Vina Docking Tool AutoDock Vina (version 1.1.2), which is a molecular docking and virtual screening application [21], was used to predict binding affinities (kcal/mol) between target proteins (ACE2 [PDB ID: 1R42] or TMPRSS2) and given chemical compounds. SMILES of given chemical compounds were converted to the PDBQT format using Open Babel (version 2.3.2) [22] with the following options: –gen3d and -p 7.4. A homology model of TMPRSS2 was built using the SWISS-MODEL server (https: //swissmodel.expasy.org/) due to the unavailability of the 3D structure in the protein data bank (PDB) [23]. AutoDockTools was used to convert coordinates of the given target proteins into the form needed for docking calculations by adding charges and hydrogen atoms,